There are many publications on the effects of
processing and preparation on the nutrient content of foods in
specific processing/food/nutrient situations [9, 42, 74, 88].
While quite detailed predictive equations have been derived for a
few of these situations [91], in general it is not possible to
predict the nutrient changes that will occur in unstudied
situations [76]. However, because of the need for food
composition data, tables of approximate factors for US foods have
been compiled for three important aspects of food preparation
[49, 97]:

removal of inedible parts (where a refuse
or yield factor corrects for removal of a portion of the
food);

gain or loss of water and fats during
cooking; and

destruction of nutrients during food
preparation.

Weight Adjustments for Inedible Parts

Foods are commonly expressed in terms either of
the "as purchased" weight of the food or of the
"as consumed" weight of the food, or, in some cases, in
both ways (e.g., the

Chinese tables [16]). Moreover, many food items
have multiple stages of preparation, and therefore multiple
stages at which they may be consumed (e.g., few persons would
consume the bones in meat, but the fat of meat may not be trimmed
by all A data base developer may have to interconvert nutrient
data between these various forms; the mechanism for this is the
"refuse factor", the ratio, expressed as a percentage,
of the weight of the "inedible" portion of a food to
the total weight of the food as purchased. It is important to
realize that this is only a weight adjustment since the nutrient
content of the inedible portion of a food often differs
significantly from the content of the edible portion.

EXAMPLE: Table 1 of the 1963 edition of
Agriculture Handbook No. 8 [95] gives nutrient values of foods in
100 g edible portions as consumed. Table 2 in this same
publication gives nutrient values in the edible portion of one
pound of food as purchased, with an additional column labelled
"Refuse". Using this factor, the nutrient data of the
two tables can be easily converted from one to the other. For
example, item number 1411, green olives, is listed as containing
20% of its weight as pits; thus an energy value of 338 calories
per 100 g edible portion converts first to 1,536 calories per
pound edible portion (338/0.22, converting from grams to pounds)
and then to 1,228 calories per edible portion of one purchased
pound (80% of 1,536) since only 80% of a pound of green olives is
edible (the 20% refuse subtracted).

Given a food/process-specific refuse factor, a
data base user or compiler can convert between "as
purchased" and "edible portions". Many tables
contain such a factor for their entries (e.g., the USDA tables
[96] give both sets of values and the factors for many entries;
the FAO African table [46] uses "g inedible per 100 g
purchased", while the Indian [25] and Chinese [16] tables
use "g edible per 100 g purchased"). Additionally,
within the United States there are a number of other sources of
such information: Agriculture Handbook No. 102 (Food Yields
Summarized by Different Stages of Preparation) [49],
Agriculture Handbook No. 8 [96, 95], and the American Home
Economics Association Handbook of Food Preparation [4].

There are two important assumptions upon which
refuse factors are based:

the proportion removed is not a function
of the size of the food (e.g., the shells of both large
and small eggs are about 12% of their weight), and

the percentage of a food declared refuse
or inedible is constant from sample to sample of the same
food. (Note that Agriculture Handbook No. 102 [49] does
give ranges of many of its refuse factors.)

Given full recognition of the
oversimplification of these assumptions, refuse factors will
permit food composition data users to convert nutrient
calculations from "as purchased" to "edible
portion".

Adjustments for Losses and Gains During
Preparation

Often, in preparation of foods for consumption,
there is a loss or gain of water, a loss of fat, or both. (When
water is gained, it may be included as an ingredient in a recipe,
and similar conventions can be used for fat gain.) Volatile
components other than moisture may be lost during preparation,
but these are seldom measured and thus are included in the
moisture loss.

If cooking time is short, or the container is
tightly covered, the water loss due to evaporation may be small;
however, if cooking time is long and the container is not
covered, the loss can be large. Evaporative losses can also occur
without cooking, if a food item is left standing uncovered for
some time. If the assumption is made that only water is lost (or
added), nutrient ratios will not change, but their density will.
These weight losses are traditionally summarized by a "yield
factor", the weight of the prepared item divided by the
weight of the unprepared item. A major compilation of yield
percentages of losses and gains appears in Agriculture Handbook
No. 102 [49]. These factors can be used to predict the nutrient
contents of prepared foods from the values in the same foods raw.

These numbers compare favorably with the
entries for cooked carrots for energy (measured value of 45
kcal), protein (measured 1.09 g), and calcium (29 mg), but not at
all with the measured ascorbic acid value of 23.

As this example shows, yield percentages
provide only rough estimates of the cooking losses that occur,
and further, one must be aware that the cooking process may
affect certain nutrients directly (e.g., ascorbic acid is
sensitive to heat and, further, may be leached into the cooking
water).

To estimate the nutrient content of an item
cooked in water, with the water drained off and discarded, it is
necessary to estimate the nutrient value of the discarded liquid.
It is likely that some water-soluble vitamins and minerals will
be lost into the discarded liquid. These losses may be estimated
from measurements made on similar foods. Drained and undrained
nutrient values are given in many food composition tables. Once
these losses are applied, the values of all nutrients must be
adjusted to reflect the new yield of the food item.

If fats (drippings) are drained, the nutrient
value of the fat can be roughly estimated from yield factors in
the same fashion as above. The situation is more complex since
both water and fat losses must be considered, and due to the
extremely high caloric density of fat, small errors can have a
large impact on nutrient values. Thus, since these estimates are
often crude, they must be used with great care.

EXAMPLE: The USDA tables [96] give 57.54 g fat
and 8.66 g protein in 100 g raw bacon. For broiling, Handbook No.
102 [49] gives a yield factor of 29%; this 71% loss is separated
into losses from drippings (49%) and from volatiles (22%).
Assuming that the drippings are all fat, this gives, after
cooking 100 g of bacon, 8.5 g (57.54 - 49.0) of fat in 29 g of
cooked bacon. This converts into 29.5 g (8.54 / 0.29) per 100 g.
By comparison, the protein conversion is a single step since
there is no loss of protein during cooking: 8.66 g protein per
100 g in raw bacon / 0.29 (the overall yield factor) = 29.0 g
protein per 100 g in broiled bacon. Comparison of these values
with measured USDA values shows that the fat calculation is not
accurate (49.24 g fat reported in cooked bacon) while the protein
value is quite acceptable (30.45 g reported). The difficulty
arises in assuming that the drippings are all fat; clearly, some
is water loss, but one cannot estimate how much from Handbook No.
102.

Loss of Nutrients During Preparation

Because of the special interest in vitamin and
mineral losses during food preparation, the USDA has published a
table of "Nutrient Retention Values" [97]. This lists
percentages of retention of nine vitamins and nine minerals for a
number of foods and cooking methods, based on standard cooking
times and temperatures. These factors are averages of a wide
range of possible values and reflect food preparation practices
in the United States. They may be used when more specific data
are unavailable. As with the factors above for refuse and yield,
these values are approximations and may not be appropriate in all
situations. Such values are available in many countries. The most
appropriate values can be obtained by comparing the cooking
methods used to develop these factors with the habitual cooking
methods in a region.

In many cases, the retention factors will
include losses due to heating and losses due to draining. If
there is also evaporative loss, a yield factor must be applied in
addition to the retention factors.

EXAMPLE: The USDA tables [96] give the folacin
level in raw onions as 19.9 µg. The retention factor of folacin
is 70% for general "preparing and draining" of root
vegetables, which predicts 13.93 µg per 100 g (19.9 x 0.70)
folacin in cooked onions. Since the cooking of 100 g of onions
yields 95 g cooked and drained onions, the final predicted
folacin level is 13.93 / 0.95 or 14.7 µg per 100 g cooked
onions. This compares with the measured value of 12.7 µg.

This example, like the previous ones, is
presented both to illustrate the calculations necessary to apply
these factors, and to show the potential inaccuracies of these
methods of estimating nutrient contents of prepared foods. In
most cases, data from similar foods modified by factors of
refuse, yield, and nutrient retention must be regarded as a
temporary measure to obtain entries in a food composition data
base until more specific foods are analysed, more extensive and
accurate theoretical procedures are developed, or both.

Most data base developers estimate some
nutrient values by assuming they are zero (i.e., not present in
any detectable amount in the food item). Often these are
decisions based on logic alone. For example, if there is no fat
in a food item, there is obviously no saturated fat; if there is
no carbohydrate, there is no sucrose. Or there may be a
biological basis for this assumption (e.g., certain nutrients
such as vitamin B-12 do not appear in plant products, while
others such as dietary fibre do not appear in animal products).
If these estimated zeros appear in a data base, an explanatory
annotation should accompany them.

Alternatively, if a nutrient is present in
trace amounts, the data base compiler must decide how to
represent that information, and must avoid misleading the user
into believing that "trace" is equivalent to
"zero". If data are "not available", the
compiler must ensure that the user will not assume a zero by
default. See page 10 for more on this topic.

Mixed dishes or multi-ingredient foods
represent the majority of items in diets worldwide. These include
not only foods prepared in the home but also foods prepared in
restaurants, by food vendors, in institutions such as hospitals,
schools, and the military, and by the food industry. To enable
dietitians, nutritionists, and epidemiologists to evaluate the
role of these foods in the health of individuals, there is a need
for composition data on these foods. Obtaining and using data on
the content of multi-ingredient foods presents a number of
inherent difficulties, primarily because of the abundance and
diversity of these kinds of foods. Many mixed dishes, as prepared
for consumption, are variable and poorly defined, differing from
kitchen to kitchen, day to day, around the world. Analytic data
do not exist for most of these foods, and accurate estimation of
their nutrient content is perhaps impossible. However, such data
are needed, and are routinely being estimated and used. For
example, in the food industry, predicted nutrient content of
proposed new product formulations is critical to decisions
regarding further work on products.

This chapter provides guidelines for estimating
the nutrient levels of multi-ingredient foods based on the
nutrient levels of the ingredients. For simplicity, a
multi-ingredient food is defined as a food with two or more
ingredients. In addition to including standard mixed dishes
such as curries, stews, casseroles, salads, and many dessert
items, the term "multi-ingredient food" is used for
simple mixtures such as foods prepared with the addition of water
(e.g., reconstituted condensed milk or gelatin dessert made from
dry powder), foods prepared with the addition of fat (e.g.,
sauteed or fried foods), and foods which have added sauces,
gravies, or toppings (e.g., asparagus with hollandaise sauce or
ice cream with chocolate syrup, whipped cream, nuts, and
cherries).

The procedure for calculating the nutrient
content of a multi-ingredient food starts from a recipe-a list of
ingredients and a description of how they are combined-and the
nutrient content of the ingredients. It is possible that some of
the nutrient values for some ingredients may be missing and must
be obtained by methods described above in this document (i.e.,
from another data base or by analogy with a similar ingredient).
It is also possible that an ingredient item may itself be a
multi-ingredient food (e.g., bread or a sauce) and may require
calculation from a recipe with its own individual components.
Types of recipes are discussed below.

Given a recipe and data on the ingredients, the
problem is then how to combine these data. Since many
multi-ingredient foods involve the processing of foods in ways
which change their nutrient content (at least on a per weight or
volume basis), calculations often employ the factors described in
the previous section:

refuse factors to correct for weight
changes due to removal of inedible portions during
preparation, cooking, or both;

corrections for weight (yield factors) and
nutrient levels due to changes in water and fat during
preparation, cooking, or both; and

nutrient retention factors to correct for
nutrient (primarily vitamin and mineral) losses or gains
during preparation, cooking, or both.

Guidelines for recipe calculations are given
below. It must be stressed that calculation of the nutrient
content of multi-ingredient foods from the nutrient data of the
ingredients are estimates and not meant to replace nutrient
values obtained by laboratory analysis. Calculating the values is
an intermediate solution until adequate analytic data become
available. A major deficiency of recipe calculation is the lack
of information on how foods and components interact. It is
obvious that they do, and such interactions are especially
important in mixed dishes; however, such information does not
currently exist. For many foods, recipe calculation may be the
only cost-effective way to obtain nutrient data. Analytic data
for multi-ingredient foods are not generally as available as they
are for single foods, with data especially lacking for ethnic and
regional dishes, many variations of homemade and restaurant-made
foods, and the numerous varieties of industrially prepared
canned, frozen, and packaged entrees. Because of the difficulties
and costs of analyses of mixtures of different food types, and
limitations on resources to adequately sample these mixtures,
calculated values that rely on a broad base of representative
samples may actually be more accurate than values derived from
laboratory analysis of one or two samples of the prepared food.

It is conceptually useful to distinguish
between recipes for simple combinations which require only mixing
of ingredients and then correction for weight or volume, and
recipes which require more extensive modifications of the data
through estimation of losses or gains of water, fat, vitamins,
and minerals. A third class of recipes includes those which
require estimation of the amounts of the ingredients, and
occasionally the nature of the ingredients themselves.

Simple Combinations

Some recipes require only the addition of the
nutrients of the specified quantities of ingredients, followed by
adjustment of the weight or volume of the final food, in order to
express the nutrient levels per standard amount of the food (such
as 100 g or household measures). In these cases the nutrient
values should be for the ingredients in an "as
consumed" form (i.e., cooked if the ingredients are cooked
and containing no refuse). Examples include:

coffee prepared from instant powder with
added sugar and cream;

baked potato with sour cream and chives;

tomato soup prepared from condensed soup
and whole milk. (Note that heating may modify the
nutrient content in this situation.)

The nutrient levels of multi-ingredient foods
that are prepared with the addition of water (such as milk
reconstituted from powder) are also of this type, and can be
estimated quite well if water (e.g., tap water, well water,
bottled spring water, distilled water, mineral water) and its
associated nutrient levels are included in the data base. If a
data base does not include water as a food item, the nutrient
contribution from water can be considered zero when calculating
the nutrient content of a multi-ingredient food; however, this
must be noted, since in some areas and for some nutrients the
contribution from water is important. The weight contribution of
water must, of course, be considered when expressing the nutrient
levels per unit of multi-ingredient food.

A special aspect of these recipes is that,
while the weight of ingredients add directly, volume often does
not, and special care must be taken to ensure that results
expressed on a per volume basis are correct. For example, one cup
of powdered milk plus three cups of water yields significantly
less than four cups of fluid milk; likewise, 1/2 cup mayonnaise
added to one cup chopped apples, Y. cup raisins, and Y. cup
chopped walnuts yields about 1.3 cups salad.

Occasionally, simple combinations may be used
to calculate the nutrient content of a food from its component
parts. In this case, the components are treated as
"ingredients". For example, the nutrient composition of
a chicken leg may be calculated from the nutrient composition and
proportions of meat, skin, and separable fat.

Recipes Which Involve Nutrient Changes

Many recipes specify the combination of
"as consumed" ingredients, but then require additional
cooking which modifies the levels or densities of the nutrients
of the foods involved. Here it is necessary to apply the various
factors to correct for these changes due to water or fat loss or
gain. Examples of these multi-ingredient items include:

refried beans, made from boiled beans,
lard, and spices which are fried in fat; and

Recipes become more complicated when starting
with the raw ingredients. These recipes require adjustment for
refuse and yield during preparation and cooking as well as
nutrient losses and gains. Consider, for example, the above
shepherd's pie when data are available only for raw meat and
vegetables.

Recipes for Which Ingredient Amounts
Must Be Estimated

Occasionally, the amounts of ingredients are
unknown and must be estimated. If partial nutritional data are
available for the recipe, these may be used to estimate
ingredient proportions, which in turn may be used to estimate the
missing nutrient data. This procedure may be necessary in the
case of proprietary food mixtures for which only some data on
content (e.g., from the package label) are available, in order to
obtain estimates for the other nutrients. These estimates are
usually less certain than the usual recipe estimates.

In the following example the zinc content of a
product estimated on the basis of information available about
proximate nutrients. A more complex example (for a
chocolatecoated ice cream bar) is given by Posati [68].

EXAMPLE: A compiler wishes to estimate the zinc
content of canned corned beef hash and has only the
manufacturer's information for proximate nutrients. The two main
ingredients are potatoes and corned beef, but the ratio is not
known. However, it is known that the product contains 11 g
carbohydrate and 8 g protein per 100 g edible portion. Assuming
all the carbohydrate is from the potatoes, and knowing that
cooked potatoes have approximately 15 g carbohydrate per 100 g, a
proportion of 11/15 or 73% potatoes (27% corned beef) can be
assumed. This assumption can be checked by calculating the
protein content. The corned beef component contains 25 g
protein/100 g, which would contribute about 7 g/100 g to the hash
(0.27 x 25). The protein in potatoes (about 2 g/100 g) would
contribute about 1 g, giving the correct total of 8 g/100 g for
the hash. Given this information, the zinc content of the hash
can be estimated if the zinc content of corned beef and of cooked
potatoes is known.

Another illustration of the procedure is the
use of known ratios to estimate amounts of individual fatty acids
and amino acids. Well-defined patterns of fatty acids for
different classes of foods can be used to calculate specific
fatty acid levels based on known total lipid content, and,
similarly, patterns of amino acids can be used to calculate
specific amino acid levels based on known total nitrogen content.

EXAMPLE: The amino acid pattern of milk can be
used to estimate the individual amino acids of cream given the
nitrogen content of cream and milk, and the assumption that cream
has the same amino acid pattern as does milk. From Paul and
Southgate [59], whole milk has 510 mg of methionine per gram of
nitrogen and single cream (21% fat) has 0.376 g of nitrogen per
100 g. From this it is estimated that the methionine content of
cream is 0.376 x 510 = 192 mg per 100 g.